Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations74
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.8 KiB
Average record size in memory827.8 B

Variable types

Numeric9
Text5
Categorical8

Alerts

balls_left is highly overall correlated with first_ings_score and 2 other fieldsHigh correlation
first_ings_score is highly overall correlated with balls_left and 3 other fieldsHigh correlation
first_ings_wkts is highly overall correlated with first_ings_score and 1 other fieldsHigh correlation
highscore is highly overall correlated with first_ings_score and 1 other fieldsHigh correlation
match_result is highly overall correlated with balls_left and 2 other fieldsHigh correlation
second_ings_score is highly overall correlated with first_ings_score and 2 other fieldsHigh correlation
second_ings_wkts is highly overall correlated with balls_leftHigh correlation
team1 is highly overall correlated with venueHigh correlation
team2 is highly overall correlated with toss_winnerHigh correlation
toss_winner is highly overall correlated with team2High correlation
venue is highly overall correlated with team1High correlation
stage is highly imbalanced (78.1%) Imbalance
match_result is highly imbalanced (75.5%) Imbalance
match_id is uniformly distributed Uniform
team1 is uniformly distributed Uniform
team2 is uniformly distributed Uniform
match_id has unique values Unique
second_ings_wkts has 2 (2.7%) zeros Zeros
balls_left has 26 (35.1%) zeros Zeros

Reproduction

Analysis started2025-06-13 15:34:16.317984
Analysis finished2025-06-13 15:34:36.652464
Duration20.33 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

match_id
Real number (ℝ)

Uniform  Unique 

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.5
Minimum1
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:36.841783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.65
Q119.25
median37.5
Q355.75
95-th percentile70.35
Maximum74
Range73
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation21.505813
Coefficient of variation (CV)0.57348835
Kurtosis-1.2
Mean37.5
Median Absolute Deviation (MAD)18.5
Skewness0
Sum2775
Variance462.5
MonotonicityStrictly increasing
2025-06-13T21:04:37.169915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
56 1
 
1.4%
54 1
 
1.4%
53 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
Other values (64) 64
86.5%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
74 1
1.4%
73 1
1.4%
72 1
1.4%
71 1
1.4%
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%

date
Text

Distinct62
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2025-06-13T21:04:37.714660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.216216
Min length10

Characters and Unicode

Total characters904
Distinct characters26
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)67.6%

Sample

1st rowMarch 22,2025
2nd rowMarch 23,2025
3rd rowMarch 23,2025
4th rowMarch 24,2025
5th rowMarch 25,2025
ValueCountFrequency (%)
april 37
25.0%
may 23
15.5%
march 12
 
8.1%
23,2025 4
 
2.7%
25,2025 4
 
2.7%
30,2025 4
 
2.7%
27,2025 4
 
2.7%
18,2025 3
 
2.0%
24,2025 3
 
2.0%
22,2025 3
 
2.0%
Other values (32) 51
34.5%
2025-06-13T21:04:38.527124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 186
20.6%
0 93
10.3%
5 82
9.1%
74
 
8.2%
, 74
 
8.2%
r 49
 
5.4%
A 37
 
4.1%
i 37
 
4.1%
l 37
 
4.1%
p 37
 
4.1%
Other values (16) 198
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 434
48.0%
Lowercase Letter 248
27.4%
Space Separator 74
 
8.2%
Other Punctuation 74
 
8.2%
Uppercase Letter 74
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 49
19.8%
i 37
14.9%
l 37
14.9%
p 37
14.9%
a 35
14.1%
y 23
9.3%
c 12
 
4.8%
h 12
 
4.8%
u 2
 
0.8%
n 2
 
0.8%
Decimal Number
ValueCountFrequency (%)
2 186
42.9%
0 93
21.4%
5 82
18.9%
1 24
 
5.5%
3 14
 
3.2%
7 8
 
1.8%
9 7
 
1.6%
8 7
 
1.6%
4 7
 
1.6%
6 6
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
A 37
50.0%
M 35
47.3%
J 2
 
2.7%
Space Separator
ValueCountFrequency (%)
74
100.0%
Other Punctuation
ValueCountFrequency (%)
, 74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 582
64.4%
Latin 322
35.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 49
15.2%
A 37
11.5%
i 37
11.5%
l 37
11.5%
p 37
11.5%
M 35
10.9%
a 35
10.9%
y 23
7.1%
c 12
 
3.7%
h 12
 
3.7%
Other values (4) 8
 
2.5%
Common
ValueCountFrequency (%)
2 186
32.0%
0 93
16.0%
5 82
14.1%
74
 
12.7%
, 74
 
12.7%
1 24
 
4.1%
3 14
 
2.4%
7 8
 
1.4%
9 7
 
1.2%
8 7
 
1.2%
Other values (2) 13
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 186
20.6%
0 93
10.3%
5 82
9.1%
74
 
8.2%
, 74
 
8.2%
r 49
 
5.4%
A 37
 
4.1%
i 37
 
4.1%
l 37
 
4.1%
p 37
 
4.1%
Other values (16) 198
21.9%

venue
Categorical

High correlation 

Distinct13
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Narendra Modi Stadium, Ahmedabad
Ekana Cricket Stadium, Lucknow
Eden Gardens, Kolkata
Wankhede Stadium, Mumbai
Sawai Mansingh Stadium, Jaipur
Other values (8)
36 

Length

Max length45
Median length33
Mean length30.527027
Min length21

Characters and Unicode

Total characters2259
Distinct characters44
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st rowEden Gardens, Kolkata
2nd rowRajiv Gandhi International Stadium, Hyderabad
3rd rowMA Chidambaram Stadium, Chennai
4th rowACA-VDCA Cricket Stadium, Vishakhapatnam
5th rowNarendra Modi Stadium, Ahmedabad

Common Values

ValueCountFrequency (%)
Narendra Modi Stadium, Ahmedabad 9
12.2%
Ekana Cricket Stadium, Lucknow 8
10.8%
Eden Gardens, Kolkata 7
9.5%
Wankhede Stadium, Mumbai 7
9.5%
Sawai Mansingh Stadium, Jaipur 7
9.5%
Arun Jaitley Stadium, Delhi 7
9.5%
Rajiv Gandhi International Stadium, Hyderabad 6
8.1%
MA Chidambaram Stadium, Chennai 6
8.1%
M. Chinnaswamy Stadium, Bangalore 6
8.1%
New PCA Cricket Stadium, Mullanpur 6
8.1%
Other values (3) 5
6.8%

Length

2025-06-13T21:04:38.823997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
stadium 67
23.0%
cricket 16
 
5.5%
narendra 9
 
3.1%
ahmedabad 9
 
3.1%
modi 9
 
3.1%
ekana 8
 
2.7%
lucknow 8
 
2.7%
mansingh 7
 
2.4%
delhi 7
 
2.4%
jaitley 7
 
2.4%
Other values (27) 144
49.5%

Most occurring characters

ValueCountFrequency (%)
a 303
 
13.4%
217
 
9.6%
i 174
 
7.7%
d 148
 
6.6%
n 129
 
5.7%
t 113
 
5.0%
u 110
 
4.9%
e 106
 
4.7%
m 104
 
4.6%
r 90
 
4.0%
Other values (34) 765
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1636
72.4%
Uppercase Letter 324
 
14.3%
Space Separator 217
 
9.6%
Other Punctuation 80
 
3.5%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 303
18.5%
i 174
10.6%
d 148
9.0%
n 129
7.9%
t 113
 
6.9%
u 110
 
6.7%
e 106
 
6.5%
m 104
 
6.4%
r 90
 
5.5%
h 62
 
3.8%
Other values (12) 297
18.2%
Uppercase Letter
ValueCountFrequency (%)
S 74
22.8%
C 45
13.9%
M 41
12.7%
A 35
10.8%
G 15
 
4.6%
E 15
 
4.6%
N 15
 
4.6%
J 14
 
4.3%
D 10
 
3.1%
B 8
 
2.5%
Other values (8) 52
16.0%
Other Punctuation
ValueCountFrequency (%)
, 74
92.5%
. 6
 
7.5%
Space Separator
ValueCountFrequency (%)
217
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1960
86.8%
Common 299
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 303
15.5%
i 174
 
8.9%
d 148
 
7.6%
n 129
 
6.6%
t 113
 
5.8%
u 110
 
5.6%
e 106
 
5.4%
m 104
 
5.3%
r 90
 
4.6%
S 74
 
3.8%
Other values (30) 609
31.1%
Common
ValueCountFrequency (%)
217
72.6%
, 74
 
24.7%
. 6
 
2.0%
- 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 303
 
13.4%
217
 
9.6%
i 174
 
7.7%
d 148
 
6.6%
n 129
 
5.7%
t 113
 
5.0%
u 110
 
4.9%
e 106
 
4.7%
m 104
 
4.6%
r 90
 
4.0%
Other values (34) 765
33.9%

team1
Categorical

High correlation  Uniform 

Distinct10
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
PBKS
KKR
GT
RCB
CSK
Other values (5)
34 

Length

Max length4
Median length3
Mean length2.7297297
Min length2

Characters and Unicode

Total characters202
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKKR
2nd rowSRH
3rd rowCSK
4th rowDC
5th rowGT

Common Values

ValueCountFrequency (%)
PBKS 9
12.2%
KKR 8
10.8%
GT 8
10.8%
RCB 8
10.8%
CSK 7
9.5%
DC 7
9.5%
RR 7
9.5%
MI 7
9.5%
LSG 7
9.5%
SRH 6
8.1%

Length

2025-06-13T21:04:39.105242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:39.437238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pbks 9
12.2%
kkr 8
10.8%
gt 8
10.8%
rcb 8
10.8%
csk 7
9.5%
dc 7
9.5%
rr 7
9.5%
mi 7
9.5%
lsg 7
9.5%
srh 6
8.1%

Most occurring characters

ValueCountFrequency (%)
R 36
17.8%
K 32
15.8%
S 29
14.4%
C 22
10.9%
B 17
8.4%
G 15
7.4%
P 9
 
4.5%
T 8
 
4.0%
D 7
 
3.5%
M 7
 
3.5%
Other values (3) 20
9.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 202
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 36
17.8%
K 32
15.8%
S 29
14.4%
C 22
10.9%
B 17
8.4%
G 15
7.4%
P 9
 
4.5%
T 8
 
4.0%
D 7
 
3.5%
M 7
 
3.5%
Other values (3) 20
9.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 202
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 36
17.8%
K 32
15.8%
S 29
14.4%
C 22
10.9%
B 17
8.4%
G 15
7.4%
P 9
 
4.5%
T 8
 
4.0%
D 7
 
3.5%
M 7
 
3.5%
Other values (3) 20
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 36
17.8%
K 32
15.8%
S 29
14.4%
C 22
10.9%
B 17
8.4%
G 15
7.4%
P 9
 
4.5%
T 8
 
4.0%
D 7
 
3.5%
M 7
 
3.5%
Other values (3) 20
9.9%

team2
Categorical

High correlation  Uniform 

Distinct10
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
MI
RCB
PBKS
SRH
RR
Other values (5)
34 

Length

Max length4
Median length3
Mean length2.7027027
Min length2

Characters and Unicode

Total characters200
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRCB
2nd rowRR
3rd rowMI
4th rowLSG
5th rowPBKS

Common Values

ValueCountFrequency (%)
MI 9
12.2%
RCB 8
10.8%
PBKS 8
10.8%
SRH 8
10.8%
RR 7
9.5%
LSG 7
9.5%
CSK 7
9.5%
GT 7
9.5%
DC 7
9.5%
KKR 6
8.1%

Length

2025-06-13T21:04:39.808056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:40.131695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mi 9
12.2%
rcb 8
10.8%
pbks 8
10.8%
srh 8
10.8%
rr 7
9.5%
lsg 7
9.5%
csk 7
9.5%
gt 7
9.5%
dc 7
9.5%
kkr 6
8.1%

Most occurring characters

ValueCountFrequency (%)
R 36
18.0%
S 30
15.0%
K 27
13.5%
C 22
11.0%
B 16
8.0%
G 14
 
7.0%
M 9
 
4.5%
I 9
 
4.5%
P 8
 
4.0%
H 8
 
4.0%
Other values (3) 21
10.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 200
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 36
18.0%
S 30
15.0%
K 27
13.5%
C 22
11.0%
B 16
8.0%
G 14
 
7.0%
M 9
 
4.5%
I 9
 
4.5%
P 8
 
4.0%
H 8
 
4.0%
Other values (3) 21
10.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 200
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 36
18.0%
S 30
15.0%
K 27
13.5%
C 22
11.0%
B 16
8.0%
G 14
 
7.0%
M 9
 
4.5%
I 9
 
4.5%
P 8
 
4.0%
H 8
 
4.0%
Other values (3) 21
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 36
18.0%
S 30
15.0%
K 27
13.5%
C 22
11.0%
B 16
8.0%
G 14
 
7.0%
M 9
 
4.5%
I 9
 
4.5%
P 8
 
4.0%
H 8
 
4.0%
Other values (3) 21
10.5%

stage
Categorical

Imbalance 

Distinct3
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
League
70 
Playoffs
 
3
Final
 
1

Length

Max length8
Median length6
Mean length6.0675676
Min length5

Characters and Unicode

Total characters449
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st rowLeague
2nd rowLeague
3rd rowLeague
4th rowLeague
5th rowLeague

Common Values

ValueCountFrequency (%)
League 70
94.6%
Playoffs 3
 
4.1%
Final 1
 
1.4%

Length

2025-06-13T21:04:40.511014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:40.745391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
league 70
94.6%
playoffs 3
 
4.1%
final 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e 140
31.2%
a 74
16.5%
L 70
15.6%
g 70
15.6%
u 70
15.6%
f 6
 
1.3%
l 4
 
0.9%
P 3
 
0.7%
y 3
 
0.7%
o 3
 
0.7%
Other values (4) 6
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 375
83.5%
Uppercase Letter 74
 
16.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 140
37.3%
a 74
19.7%
g 70
18.7%
u 70
18.7%
f 6
 
1.6%
l 4
 
1.1%
y 3
 
0.8%
o 3
 
0.8%
s 3
 
0.8%
i 1
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
L 70
94.6%
P 3
 
4.1%
F 1
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 449
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 140
31.2%
a 74
16.5%
L 70
15.6%
g 70
15.6%
u 70
15.6%
f 6
 
1.3%
l 4
 
0.9%
P 3
 
0.7%
y 3
 
0.7%
o 3
 
0.7%
Other values (4) 6
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 140
31.2%
a 74
16.5%
L 70
15.6%
g 70
15.6%
u 70
15.6%
f 6
 
1.3%
l 4
 
0.9%
P 3
 
0.7%
y 3
 
0.7%
o 3
 
0.7%
Other values (4) 6
 
1.3%

toss_winner
Categorical

High correlation 

Distinct10
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
PBKS
13 
DC
SRH
RCB
RR
Other values (5)
31 

Length

Max length4
Median length3
Mean length2.7837838
Min length2

Characters and Unicode

Total characters206
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRCB
2nd rowRR
3rd rowCSK
4th rowDC
5th rowGT

Common Values

ValueCountFrequency (%)
PBKS 13
17.6%
DC 8
10.8%
SRH 8
10.8%
RCB 7
9.5%
RR 7
9.5%
GT 7
9.5%
MI 7
9.5%
CSK 6
8.1%
KKR 6
8.1%
LSG 5
 
6.8%

Length

2025-06-13T21:04:41.026647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:41.339099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pbks 13
17.6%
dc 8
10.8%
srh 8
10.8%
rcb 7
9.5%
rr 7
9.5%
gt 7
9.5%
mi 7
9.5%
csk 6
8.1%
kkr 6
8.1%
lsg 5
 
6.8%

Most occurring characters

ValueCountFrequency (%)
R 35
17.0%
S 32
15.5%
K 31
15.0%
C 21
10.2%
B 20
9.7%
P 13
 
6.3%
G 12
 
5.8%
D 8
 
3.9%
H 8
 
3.9%
T 7
 
3.4%
Other values (3) 19
9.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 206
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 35
17.0%
S 32
15.5%
K 31
15.0%
C 21
10.2%
B 20
9.7%
P 13
 
6.3%
G 12
 
5.8%
D 8
 
3.9%
H 8
 
3.9%
T 7
 
3.4%
Other values (3) 19
9.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 206
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 35
17.0%
S 32
15.5%
K 31
15.0%
C 21
10.2%
B 20
9.7%
P 13
 
6.3%
G 12
 
5.8%
D 8
 
3.9%
H 8
 
3.9%
T 7
 
3.4%
Other values (3) 19
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 35
17.0%
S 32
15.5%
K 31
15.0%
C 21
10.2%
B 20
9.7%
P 13
 
6.3%
G 12
 
5.8%
D 8
 
3.9%
H 8
 
3.9%
T 7
 
3.4%
Other values (3) 19
9.2%

toss_decision
Categorical

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
Bowl
61 
Bat
13 

Length

Max length4
Median length4
Mean length3.8243243
Min length3

Characters and Unicode

Total characters283
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBowl
2nd rowBowl
3rd rowBowl
4th rowBowl
5th rowBowl

Common Values

ValueCountFrequency (%)
Bowl 61
82.4%
Bat 13
 
17.6%

Length

2025-06-13T21:04:41.979782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:42.241715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
bowl 61
82.4%
bat 13
 
17.6%

Most occurring characters

ValueCountFrequency (%)
B 74
26.1%
o 61
21.6%
w 61
21.6%
l 61
21.6%
a 13
 
4.6%
t 13
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 209
73.9%
Uppercase Letter 74
 
26.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 61
29.2%
w 61
29.2%
l 61
29.2%
a 13
 
6.2%
t 13
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
B 74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 283
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 74
26.1%
o 61
21.6%
w 61
21.6%
l 61
21.6%
a 13
 
4.6%
t 13
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 74
26.1%
o 61
21.6%
w 61
21.6%
l 61
21.6%
a 13
 
4.6%
t 13
 
4.6%

first_ings_score
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.75342
Minimum95
Maximum286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:42.496278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile114.25
Q1166.75
median193
Q3212
95-th percentile239.75
Maximum286
Range191
Interquartile range (IQR)45.25

Descriptive statistics

Standard deviation37.231884
Coefficient of variation (CV)0.19621192
Kurtosis0.66838054
Mean189.75342
Median Absolute Deviation (MAD)23
Skewness-0.30641597
Sum14041.753
Variance1386.2132
MonotonicityNot monotonic
2025-06-13T21:04:42.808781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205 4
 
5.4%
180 3
 
4.1%
203 3
 
4.1%
190 3
 
4.1%
162 2
 
2.7%
163 2
 
2.7%
206 2
 
2.7%
217 2
 
2.7%
219 2
 
2.7%
196 2
 
2.7%
Other values (47) 49
66.2%
ValueCountFrequency (%)
95 1
1.4%
101 1
1.4%
103 1
1.4%
111 1
1.4%
116 1
1.4%
133 1
1.4%
143 1
1.4%
151 1
1.4%
152 1
1.4%
154 1
1.4%
ValueCountFrequency (%)
286 1
1.4%
278 1
1.4%
245 1
1.4%
243 1
1.4%
238 1
1.4%
236 1
1.4%
235 1
1.4%
231 1
1.4%
230 1
1.4%
228 1
1.4%

first_ings_wkts
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4520548
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:43.074403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median6
Q38
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0939646
Coefficient of variation (CV)0.32454228
Kurtosis-0.73172408
Mean6.4520548
Median Absolute Deviation (MAD)2
Skewness-0.082541906
Sum477.45205
Variance4.3846876
MonotonicityNot monotonic
2025-06-13T21:04:43.324371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 14
18.9%
6 13
17.6%
8 12
16.2%
9 8
10.8%
7 8
10.8%
10 6
8.1%
4 5
 
6.8%
3 5
 
6.8%
2 2
 
2.7%
6.452054795 1
 
1.4%
ValueCountFrequency (%)
2 2
 
2.7%
3 5
 
6.8%
4 5
 
6.8%
5 14
18.9%
6 13
17.6%
6.452054795 1
 
1.4%
7 8
10.8%
8 12
16.2%
9 8
10.8%
10 6
8.1%
ValueCountFrequency (%)
10 6
8.1%
9 8
10.8%
8 12
16.2%
7 8
10.8%
6.452054795 1
 
1.4%
6 13
17.6%
5 14
18.9%
4 5
 
6.8%
3 5
 
6.8%
2 2
 
2.7%

second_ings_score
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.01389
Minimum7
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:43.666792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile106.65
Q1158.25
median177
Q3200.5
95-th percentile230.7
Maximum247
Range240
Interquartile range (IQR)42.25

Descriptive statistics

Standard deviation38.269947
Coefficient of variation (CV)0.21992467
Kurtosis4.0340081
Mean174.01389
Median Absolute Deviation (MAD)20.5
Skewness-1.2847105
Sum12877.028
Variance1464.5889
MonotonicityNot monotonic
2025-06-13T21:04:44.195600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177 3
 
4.1%
159 3
 
4.1%
208 2
 
2.7%
161 2
 
2.7%
188 2
 
2.7%
205 2
 
2.7%
168 2
 
2.7%
174.0138889 2
 
2.7%
186 2
 
2.7%
147 2
 
2.7%
Other values (42) 52
70.3%
ValueCountFrequency (%)
7 1
1.4%
95 1
1.4%
98 1
1.4%
106 1
1.4%
107 1
1.4%
117 1
1.4%
120 1
1.4%
121 2
2.7%
146 2
2.7%
147 2
2.7%
ValueCountFrequency (%)
247 1
1.4%
242 1
1.4%
234 1
1.4%
232 1
1.4%
230 1
1.4%
212 1
1.4%
211 2
2.7%
209 2
2.7%
208 2
2.7%
207 1
1.4%

second_ings_wkts
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5138889
Minimum0
Maximum10
Zeros2
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:44.619703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.65
Q13
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8428861
Coefficient of variation (CV)0.5155864
Kurtosis-0.96715063
Mean5.5138889
Median Absolute Deviation (MAD)2
Skewness0.11377132
Sum408.02778
Variance8.0820015
MonotonicityNot monotonic
2025-06-13T21:04:44.869705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 11
14.9%
10 10
13.5%
2 9
12.2%
6 8
10.8%
4 8
10.8%
3 7
9.5%
9 6
8.1%
7 5
6.8%
8 4
 
5.4%
1 2
 
2.7%
Other values (2) 4
 
5.4%
ValueCountFrequency (%)
0 2
 
2.7%
1 2
 
2.7%
2 9
12.2%
3 7
9.5%
4 8
10.8%
5 11
14.9%
5.513888889 2
 
2.7%
6 8
10.8%
7 5
6.8%
8 4
 
5.4%
ValueCountFrequency (%)
10 10
13.5%
9 6
8.1%
8 4
 
5.4%
7 5
6.8%
6 8
10.8%
5.513888889 2
 
2.7%
5 11
14.9%
4 8
10.8%
3 7
9.5%
2 9
12.2%

match_result
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
completed
71 
tied
 
3

Length

Max length9
Median length9
Mean length8.7972973
Min length4

Characters and Unicode

Total characters651
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcompleted
2nd rowcompleted
3rd rowcompleted
4th rowcompleted
5th rowcompleted

Common Values

ValueCountFrequency (%)
completed 71
95.9%
tied 3
 
4.1%

Length

2025-06-13T21:04:45.135358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:45.354078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
completed 71
95.9%
tied 3
 
4.1%

Most occurring characters

ValueCountFrequency (%)
e 145
22.3%
t 74
11.4%
d 74
11.4%
c 71
10.9%
o 71
10.9%
m 71
10.9%
p 71
10.9%
l 71
10.9%
i 3
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 651
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 145
22.3%
t 74
11.4%
d 74
11.4%
c 71
10.9%
o 71
10.9%
m 71
10.9%
p 71
10.9%
l 71
10.9%
i 3
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 651
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 145
22.3%
t 74
11.4%
d 74
11.4%
c 71
10.9%
o 71
10.9%
m 71
10.9%
p 71
10.9%
l 71
10.9%
i 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 145
22.3%
t 74
11.4%
d 74
11.4%
c 71
10.9%
o 71
10.9%
m 71
10.9%
p 71
10.9%
l 71
10.9%
i 3
 
0.5%

match_winner
Categorical

Distinct10
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
PBKS
14 
RCB
10 
GT
DC
MI
Other values (5)
25 

Length

Max length4
Median length3
Mean length2.7972973
Min length2

Characters and Unicode

Total characters207
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRCB
2nd rowSRH
3rd rowCSK
4th rowDC
5th rowPBKS

Common Values

ValueCountFrequency (%)
PBKS 14
18.9%
RCB 10
13.5%
GT 9
12.2%
DC 8
10.8%
MI 8
10.8%
SRH 6
8.1%
LSG 6
8.1%
KKR 5
 
6.8%
CSK 4
 
5.4%
RR 4
 
5.4%

Length

2025-06-13T21:04:45.635250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:45.947722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pbks 14
18.9%
rcb 10
13.5%
gt 9
12.2%
dc 8
10.8%
mi 8
10.8%
srh 6
8.1%
lsg 6
8.1%
kkr 5
 
6.8%
csk 4
 
5.4%
rr 4
 
5.4%

Most occurring characters

ValueCountFrequency (%)
S 30
14.5%
R 29
14.0%
K 28
13.5%
B 24
11.6%
C 22
10.6%
G 15
7.2%
P 14
6.8%
T 9
 
4.3%
D 8
 
3.9%
M 8
 
3.9%
Other values (3) 20
9.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 207
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 30
14.5%
R 29
14.0%
K 28
13.5%
B 24
11.6%
C 22
10.6%
G 15
7.2%
P 14
6.8%
T 9
 
4.3%
D 8
 
3.9%
M 8
 
3.9%
Other values (3) 20
9.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 207
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 30
14.5%
R 29
14.0%
K 28
13.5%
B 24
11.6%
C 22
10.6%
G 15
7.2%
P 14
6.8%
T 9
 
4.3%
D 8
 
3.9%
M 8
 
3.9%
Other values (3) 20
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 30
14.5%
R 29
14.0%
K 28
13.5%
B 24
11.6%
C 22
10.6%
G 15
7.2%
P 14
6.8%
T 9
 
4.3%
D 8
 
3.9%
M 8
 
3.9%
Other values (3) 20
9.7%

wb_runs
Real number (ℝ)

Distinct28
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.181818
Minimum1
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:46.290066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.3
Q133.181818
median33.181818
Q333.181818
95-th percentile66.35
Maximum110
Range109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.19864
Coefficient of variation (CV)0.57858916
Kurtosis4.9056585
Mean33.181818
Median Absolute Deviation (MAD)0
Skewness1.573801
Sum2455.4545
Variance368.5878
MonotonicityNot monotonic
2025-06-13T21:04:46.572561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
33.18181818 41
55.4%
12 3
 
4.1%
11 2
 
2.7%
50 2
 
2.7%
6 2
 
2.7%
2 2
 
2.7%
100 1
 
1.4%
110 1
 
1.4%
83 1
 
1.4%
42 1
 
1.4%
Other values (18) 18
24.3%
ValueCountFrequency (%)
1 1
 
1.4%
2 2
2.7%
4 1
 
1.4%
6 2
2.7%
10 1
 
1.4%
11 2
2.7%
12 3
4.1%
14 1
 
1.4%
16 1
 
1.4%
18 1
 
1.4%
ValueCountFrequency (%)
110 1
1.4%
100 1
1.4%
83 1
1.4%
80 1
1.4%
59 1
1.4%
58 1
1.4%
54 1
1.4%
50 2
2.7%
44 1
1.4%
42 1
1.4%

wb_wickets
Real number (ℝ)

Distinct11
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3243243
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:46.806978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16.3243243
median6.3243243
Q36.8310811
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)0.50675676

Descriptive statistics

Standard deviation1.3853841
Coefficient of variation (CV)0.21905646
Kurtosis3.8812149
Mean6.3243243
Median Absolute Deviation (MAD)0.16216216
Skewness-0.98266488
Sum468
Variance1.9192892
MonotonicityNot monotonic
2025-06-13T21:04:47.025695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
6.324324324 37
50.0%
8 9
 
12.2%
7 7
 
9.5%
6 7
 
9.5%
5 5
 
6.8%
4 3
 
4.1%
9 2
 
2.7%
1 1
 
1.4%
3 1
 
1.4%
2 1
 
1.4%
ValueCountFrequency (%)
1 1
 
1.4%
2 1
 
1.4%
3 1
 
1.4%
4 3
 
4.1%
5 5
 
6.8%
6 7
 
9.5%
6.324324324 37
50.0%
7 7
 
9.5%
8 9
 
12.2%
9 2
 
2.7%
ValueCountFrequency (%)
10 1
 
1.4%
9 2
 
2.7%
8 9
 
12.2%
7 7
 
9.5%
6.324324324 37
50.0%
6 7
 
9.5%
5 5
 
6.8%
4 3
 
4.1%
3 1
 
1.4%
2 1
 
1.4%

balls_left
Real number (ℝ)

High correlation  Zeros 

Distinct27
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.819444
Minimum0
Maximum114
Zeros26
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:47.260070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q313
95-th percentile33.9
Maximum114
Range114
Interquartile range (IQR)13

Descriptive statistics

Standard deviation17.431138
Coefficient of variation (CV)1.6110936
Kurtosis17.239859
Mean10.819444
Median Absolute Deviation (MAD)6
Skewness3.5903929
Sum800.63889
Variance303.84456
MonotonicityNot monotonic
2025-06-13T21:04:47.525527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 26
35.1%
9 4
 
5.4%
3 4
 
5.4%
6 3
 
4.1%
8 3
 
4.1%
13 3
 
4.1%
10 2
 
2.7%
10.81944444 2
 
2.7%
2 2
 
2.7%
26 2
 
2.7%
Other values (17) 23
31.1%
ValueCountFrequency (%)
0 26
35.1%
1 1
 
1.4%
2 2
 
2.7%
3 4
 
5.4%
4 2
 
2.7%
5 1
 
1.4%
6 3
 
4.1%
7 1
 
1.4%
8 3
 
4.1%
9 4
 
5.4%
ValueCountFrequency (%)
114 1
1.4%
60 1
1.4%
59 1
1.4%
43 1
1.4%
29 1
1.4%
26 2
2.7%
25 1
1.4%
24 1
1.4%
23 2
2.7%
22 2
2.7%
Distinct53
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2025-06-13T21:04:48.307310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length12.783784
Min length8

Characters and Unicode

Total characters946
Distinct characters45
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)50.0%

Sample

1st rowKrunal Pandya
2nd rowIshan Kishan
3rd rowNoor Ahmad
4th rowAshutosh Sharma
5th rowShreyas Iyer
ValueCountFrequency (%)
sharma 8
 
5.4%
krunal 6
 
4.0%
pandya 6
 
4.0%
iyer 3
 
2.0%
singh 3
 
2.0%
shreyas 3
 
2.0%
mitchell 3
 
2.0%
rahul 2
 
1.3%
vaibhav 2
 
1.3%
josh 2
 
1.3%
Other values (87) 111
74.5%
2025-06-13T21:04:49.293065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 128
 
13.5%
h 75
 
7.9%
75
 
7.9%
r 74
 
7.8%
i 58
 
6.1%
n 50
 
5.3%
s 43
 
4.5%
l 42
 
4.4%
e 40
 
4.2%
S 34
 
3.6%
Other values (35) 327
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 720
76.1%
Uppercase Letter 151
 
16.0%
Space Separator 75
 
7.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 128
17.8%
h 75
10.4%
r 74
10.3%
i 58
 
8.1%
n 50
 
6.9%
s 43
 
6.0%
l 42
 
5.8%
e 40
 
5.6%
u 29
 
4.0%
d 27
 
3.8%
Other values (14) 154
21.4%
Uppercase Letter
ValueCountFrequency (%)
S 34
22.5%
K 19
12.6%
P 16
10.6%
A 13
 
8.6%
R 12
 
7.9%
M 10
 
6.6%
J 7
 
4.6%
N 6
 
4.0%
I 6
 
4.0%
H 4
 
2.6%
Other values (10) 24
15.9%
Space Separator
ValueCountFrequency (%)
75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 871
92.1%
Common 75
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 128
14.7%
h 75
 
8.6%
r 74
 
8.5%
i 58
 
6.7%
n 50
 
5.7%
s 43
 
4.9%
l 42
 
4.8%
e 40
 
4.6%
S 34
 
3.9%
u 29
 
3.3%
Other values (34) 298
34.2%
Common
ValueCountFrequency (%)
75
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 946
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 128
 
13.5%
h 75
 
7.9%
75
 
7.9%
r 74
 
7.8%
i 58
 
6.1%
n 50
 
5.3%
s 43
 
4.5%
l 42
 
4.4%
e 40
 
4.2%
S 34
 
3.6%
Other values (35) 327
34.6%
Distinct41
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2025-06-13T21:04:49.993947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length16.5
Mean length13.135135
Min length8

Characters and Unicode

Total characters972
Distinct characters45
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)28.4%

Sample

1st rowVirat Kohli
2nd rowIshan Kishan
3rd rowRachin Ravindra
4th rowNicholas Pooran
5th rowShreyas Iyer
ValueCountFrequency (%)
nicholas 7
 
4.7%
pooran 7
 
4.7%
virat 4
 
2.7%
kohli 4
 
2.7%
sharma 4
 
2.7%
kishan 3
 
2.0%
singh 3
 
2.0%
iyer 3
 
2.0%
jaiswal 3
 
2.0%
yashasvi 3
 
2.0%
Other values (71) 109
72.7%
2025-06-13T21:04:51.025192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 133
13.7%
i 80
 
8.2%
h 76
 
7.8%
76
 
7.8%
s 59
 
6.1%
n 58
 
6.0%
r 57
 
5.9%
l 45
 
4.6%
o 35
 
3.6%
e 34
 
3.5%
Other values (35) 319
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 745
76.6%
Uppercase Letter 151
 
15.5%
Space Separator 76
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 133
17.9%
i 80
10.7%
h 76
10.2%
s 59
7.9%
n 58
7.8%
r 57
7.7%
l 45
 
6.0%
o 35
 
4.7%
e 34
 
4.6%
t 26
 
3.5%
Other values (13) 142
19.1%
Uppercase Letter
ValueCountFrequency (%)
S 27
17.9%
R 21
13.9%
P 17
11.3%
K 15
9.9%
N 12
7.9%
V 8
 
5.3%
I 7
 
4.6%
J 7
 
4.6%
A 6
 
4.0%
Y 5
 
3.3%
Other values (11) 26
17.2%
Space Separator
ValueCountFrequency (%)
76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 896
92.2%
Common 76
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 133
14.8%
i 80
 
8.9%
h 76
 
8.5%
s 59
 
6.6%
n 58
 
6.5%
r 57
 
6.4%
l 45
 
5.0%
o 35
 
3.9%
e 34
 
3.8%
S 27
 
3.0%
Other values (34) 292
32.6%
Common
ValueCountFrequency (%)
76
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 133
13.7%
i 80
 
8.2%
h 76
 
7.8%
76
 
7.8%
s 59
 
6.1%
n 58
 
6.0%
r 57
 
5.9%
l 45
 
4.6%
o 35
 
3.6%
e 34
 
3.5%
Other values (35) 319
32.8%

highscore
Real number (ℝ)

High correlation 

Distinct45
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.549296
Minimum37
Maximum141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:51.322069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile47.9
Q161
median73
Q387.75
95-th percentile106.7
Maximum141
Range104
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation19.671124
Coefficient of variation (CV)0.26386733
Kurtosis0.81170109
Mean74.549296
Median Absolute Deviation (MAD)13.5
Skewness0.73905679
Sum5516.6479
Variance386.95312
MonotonicityNot monotonic
2025-06-13T21:04:51.618809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
73 5
 
6.8%
61 4
 
5.4%
57 3
 
4.1%
97 3
 
4.1%
70 3
 
4.1%
74.54929577 3
 
4.1%
67 3
 
4.1%
94 2
 
2.7%
87 2
 
2.7%
76 2
 
2.7%
Other values (35) 44
59.5%
ValueCountFrequency (%)
37 1
 
1.4%
40 1
 
1.4%
44 2
2.7%
50 1
 
1.4%
51 2
2.7%
52 1
 
1.4%
53 1
 
1.4%
56 1
 
1.4%
57 3
4.1%
58 2
2.7%
ValueCountFrequency (%)
141 1
 
1.4%
118 1
 
1.4%
117 1
 
1.4%
108 1
 
1.4%
106 1
 
1.4%
105 1
 
1.4%
103 1
 
1.4%
101 1
 
1.4%
97 3
4.1%
95 1
 
1.4%
Distinct42
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2025-06-13T21:04:52.355687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length13.621622
Min length10

Characters and Unicode

Total characters1008
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)32.4%

Sample

1st rowKrunal Pandya
2nd rowTushar Deshpande
3rd rowNoor Ahmad
4th rowMitchell Starc
5th rowSai Kishore
ValueCountFrequency (%)
prasidh 8
 
5.4%
krishna 8
 
5.4%
pandya 5
 
3.4%
krunal 4
 
2.7%
arshdeep 4
 
2.7%
singh 4
 
2.7%
bumrah 3
 
2.0%
mitchell 3
 
2.0%
sharma 3
 
2.0%
hazlewood 3
 
2.0%
Other values (68) 103
69.6%
2025-06-13T21:04:53.419148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 138
13.7%
r 87
 
8.6%
h 83
 
8.2%
74
 
7.3%
n 55
 
5.5%
i 54
 
5.4%
e 50
 
5.0%
s 49
 
4.9%
d 41
 
4.1%
u 35
 
3.5%
Other values (36) 342
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 782
77.6%
Uppercase Letter 150
 
14.9%
Space Separator 74
 
7.3%
Other Punctuation 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 138
17.6%
r 87
11.1%
h 83
10.6%
n 55
 
7.0%
i 54
 
6.9%
e 50
 
6.4%
s 49
 
6.3%
d 41
 
5.2%
u 35
 
4.5%
l 34
 
4.3%
Other values (13) 156
19.9%
Uppercase Letter
ValueCountFrequency (%)
K 23
15.3%
S 18
12.0%
P 17
11.3%
A 15
10.0%
M 10
 
6.7%
J 10
 
6.7%
H 9
 
6.0%
C 7
 
4.7%
B 7
 
4.7%
N 6
 
4.0%
Other values (11) 28
18.7%
Space Separator
ValueCountFrequency (%)
74
100.0%
Other Punctuation
ValueCountFrequency (%)
' 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 932
92.5%
Common 76
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 138
14.8%
r 87
 
9.3%
h 83
 
8.9%
n 55
 
5.9%
i 54
 
5.8%
e 50
 
5.4%
s 49
 
5.3%
d 41
 
4.4%
u 35
 
3.8%
l 34
 
3.6%
Other values (34) 306
32.8%
Common
ValueCountFrequency (%)
74
97.4%
' 2
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 138
13.7%
r 87
 
8.6%
h 83
 
8.2%
74
 
7.3%
n 55
 
5.5%
i 54
 
5.4%
e 50
 
5.0%
s 49
 
4.9%
d 41
 
4.1%
u 35
 
3.5%
Other values (36) 342
33.9%
Distinct54
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2025-06-13T21:04:53.977623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9864865
Min length4

Characters and Unicode

Total characters369
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)54.1%

Sample

1st row3--29
2nd row3--44
3rd row4--18
4th row3--42
5th row3--30
ValueCountFrequency (%)
2--25 7
 
9.5%
3--29 3
 
4.1%
3--24 2
 
2.7%
3--30 2
 
2.7%
2--17 2
 
2.7%
3--21 2
 
2.7%
3--36 2
 
2.7%
2--18 2
 
2.7%
4--28 2
 
2.7%
2--32 2
 
2.7%
Other values (44) 48
64.9%
2025-06-13T21:04:54.930589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 148
40.1%
2 61
16.5%
3 58
 
15.7%
4 33
 
8.9%
1 21
 
5.7%
5 16
 
4.3%
8 8
 
2.2%
7 8
 
2.2%
9 6
 
1.6%
6 6
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 221
59.9%
Dash Punctuation 148
40.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 61
27.6%
3 58
26.2%
4 33
14.9%
1 21
 
9.5%
5 16
 
7.2%
8 8
 
3.6%
7 8
 
3.6%
9 6
 
2.7%
6 6
 
2.7%
0 4
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 369
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 148
40.1%
2 61
16.5%
3 58
 
15.7%
4 33
 
8.9%
1 21
 
5.7%
5 16
 
4.3%
8 8
 
2.2%
7 8
 
2.2%
9 6
 
1.6%
6 6
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 369
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 148
40.1%
2 61
16.5%
3 58
 
15.7%
4 33
 
8.9%
1 21
 
5.7%
5 16
 
4.3%
8 8
 
2.2%
7 8
 
2.2%
9 6
 
1.6%
6 6
 
1.6%

Interactions

2025-06-13T21:04:33.121670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:18.327920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:20.297512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:22.251318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-06-13T21:04:25.888416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:27.731878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:29.414938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:31.402177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-06-13T21:04:20.653838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-06-13T21:04:26.096416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:27.919384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-06-13T21:04:18.749161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-06-13T21:04:32.206084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:34.238868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:19.358536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:21.482877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-06-13T21:04:25.060451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:26.982010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:28.699372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:30.378101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:32.393593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:34.431479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-06-13T21:04:25.263557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-06-13T21:04:28.871257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:30.558445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:32.574833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:35.135930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:19.733184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:21.857868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:23.669970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:25.466523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:27.356996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:29.043133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:30.792804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:32.762292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:35.307797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:19.953307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:22.048193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:23.857469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:25.685279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:27.544377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:29.230628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:31.120929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:32.934205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-06-13T21:04:55.274382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
balls_leftfirst_ings_scorefirst_ings_wktshighscorematch_idmatch_resultmatch_winnersecond_ings_scoresecond_ings_wktsstageteam1team2toss_decisiontoss_winnervenuewb_runswb_wickets
balls_left1.000-0.5390.271-0.223-0.0520.5160.000-0.453-0.5070.1160.0370.0000.2040.0000.0000.2630.322
first_ings_score-0.5391.000-0.5950.5290.2010.2520.1890.6520.4440.0000.1810.0760.0000.0000.1760.040-0.098
first_ings_wkts0.271-0.5951.000-0.436-0.2720.5470.139-0.317-0.2150.0000.1690.1140.2030.1050.187-0.052-0.090
highscore-0.2230.529-0.4361.0000.0470.0680.0360.552-0.0590.1030.0000.0000.2260.0000.000-0.1210.196
match_id-0.0520.201-0.2720.0471.0000.2890.0000.1420.1340.3400.0000.0000.1840.0000.0000.029-0.137
match_result0.5160.2520.5470.0680.2891.0000.2420.5260.2470.0000.0000.0000.0000.0000.0000.0000.000
match_winner0.0000.1890.1390.0360.0000.2421.0000.0000.0000.0000.2630.4670.1360.4980.1960.1150.000
second_ings_score-0.4530.652-0.3170.5520.1420.5260.0001.000-0.0080.0000.0000.0000.1920.0000.000-0.421-0.106
second_ings_wkts-0.5070.444-0.215-0.0590.1340.2470.000-0.0081.0000.0460.0930.1260.0000.1560.1040.139-0.475
stage0.1160.0000.0000.1030.3400.0000.0000.0000.0461.0000.1040.1040.0000.0000.0130.0000.000
team10.0370.1810.1690.0000.0000.0000.2630.0000.0930.1041.0000.0000.1850.2290.8640.0640.047
team20.0000.0760.1140.0000.0000.0000.4670.0000.1260.1040.0001.0000.0340.5620.0000.1080.003
toss_decision0.2040.0000.2030.2260.1840.0000.1360.1920.0000.0000.1850.0341.0000.1060.1940.2290.000
toss_winner0.0000.0000.1050.0000.0000.0000.4980.0000.1560.0000.2290.5620.1061.0000.2280.0490.000
venue0.0000.1760.1870.0000.0000.0000.1960.0000.1040.0130.8640.0000.1940.2281.0000.0280.130
wb_runs0.2630.040-0.052-0.1210.0290.0000.115-0.4210.1390.0000.0640.1080.2290.0490.0281.0000.002
wb_wickets0.322-0.098-0.0900.196-0.1370.0000.000-0.106-0.4750.0000.0470.0030.0000.0000.1300.0021.000

Missing values

2025-06-13T21:04:35.652443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-13T21:04:36.365607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

match_iddatevenueteam1team2stagetoss_winnertoss_decisionfirst_ings_scorefirst_ings_wktssecond_ings_scoresecond_ings_wktsmatch_resultmatch_winnerwb_runswb_wicketsballs_leftplayer_of_the_matchtop_scorerhighscorebest_bowlingbest_bowling_figure
01March 22,2025Eden Gardens, KolkataKKRRCBLeagueRCBBowl174.08.0177.03.0completedRCB33.1818187.00000022.0Krunal PandyaVirat Kohli59.0Krunal Pandya3--29
12March 23,2025Rajiv Gandhi International Stadium, HyderabadSRHRRLeagueRRBowl286.06.0242.06.0completedSRH44.0000006.3243240.0Ishan KishanIshan Kishan106.0Tushar Deshpande3--44
23March 23,2025MA Chidambaram Stadium, ChennaiCSKMILeagueCSKBowl155.09.0158.06.0completedCSK33.1818184.0000005.0Noor AhmadRachin Ravindra65.0Noor Ahmad4--18
34March 24,2025ACA-VDCA Cricket Stadium, VishakhapatnamDCLSGLeagueDCBowl209.08.0211.09.0completedDC33.1818181.0000003.0Ashutosh SharmaNicholas Pooran75.0Mitchell Starc3--42
45March 25,2025Narendra Modi Stadium, AhmedabadGTPBKSLeagueGTBowl243.05.0232.05.0completedPBKS11.0000006.3243240.0Shreyas IyerShreyas Iyer97.0Sai Kishore3--30
56March 26,2025Barsapara Stadium, GuwahatiRRKKRLeagueKKRBowl151.09.0153.02.0completedKKR33.1818188.00000015.0Quinton de KockQuinton de Kock97.0Varun Chakravarthy2--17
67March 27,2025Rajiv Gandhi International Stadium, HyderabadSRHLSGLeagueLSGBowl190.09.0193.05.0completedLSG33.1818185.00000023.0Shardul ThakurNicholas Pooran70.0Shardul Thakur4--34
78March 28,2025MA Chidambaram Stadium, ChennaiCSKRCBLeagueCSKBowl196.07.0146.08.0completedRCB50.0000006.3243240.0Rajat PatidarRajat Patidar51.0Josh Hazlewood3--21
89March 29,2025Narendra Modi Stadium, AhmedabadGTMILeagueMIBowl196.08.0160.06.0completedGT36.0000006.3243240.0Prasidh KrishnaSai Sudarshan63.0Prasidh Krishna2--18
910March 30,2025ACA-VDCA Cricket Stadium, VishakhapatnamDCSRHLeagueSRHBat163.010.0166.03.0completedDC33.1818187.00000024.0Mitchell StarcAniket Verma74.0Mitchell Starc5--35
match_iddatevenueteam1team2stagetoss_winnertoss_decisionfirst_ings_scorefirst_ings_wktssecond_ings_scoresecond_ings_wktsmatch_resultmatch_winnerwb_runswb_wicketsballs_leftplayer_of_the_matchtop_scorerhighscorebest_bowlingbest_bowling_figure
6465May 23,2025Ekana Cricket Stadium, LucknowRCBSRHLeagueRCBBowl231.06.0189.010.0completedSRH42.0000006.3243241.0Ishan KishanIshan Kishan94.0Pat Cummins3--28
6566May 24,2025Sawai Mansingh Stadium, JaipurPBKSDCLeagueDCBowl206.08.0208.04.0completedDC33.1818186.0000003.0Sameer RizviSameer Rizvi58.0Mustafizur Rahman3--33
6667May 25,2025Narendra Modi Stadium, AhmedabadGTCSKLeagueCSKBat230.05.0147.010.0completedCSK83.0000006.3243249.0Dewald BrevisDewald Brevis57.0Noor Ahmad3--21
6768May 25,2025Arun Jaitley Stadium, DelhiKKRSRHLeagueSRHBat278.03.0168.010.0completedSRH110.0000006.3243248.0Heinrich KlassenHeinrich Klassen105.0Jaydev Unadkat3--24
6869May 26,2025Sawai Mansingh Stadium, JaipurPBKSMILeaguePBKSBowl184.07.0187.03.0completedPBKS33.1818187.0000009.0Josh InglisJosh Inglis73.0Arshdeep Singh2--28
6970May 27,2025Ekana Cricket Stadium, LucknowLSGRCBLeagueRCBBowl227.03.0230.04.0completedRCB33.1818186.0000008.0Jitesh SharmaRishabh Pant118.0Will O'Rourke2--74
7071May 29,2025New PCA Cricket Stadium, MullanpurPBKSRCBPlayoffsRCBBowl101.010.0106.02.0completedRCB33.1818188.00000060.0Suyash SharmaPhil Salt56.0Suyash Sharma3--17
7172May 30,2025New PCA Cricket Stadium, MullanpurGTMIPlayoffsMIBat228.05.0208.06.0completedMI20.0000006.3243240.0Rohit SharmaRohit Sharma81.0Sai Kishore2--42
7273June 1,2025Narendra Modi Stadium, AhmedabadPBKSMIPlayoffsPBKSBowl203.06.0207.05.0completedPBKS33.1818185.0000006.0Shreyas IyerShreyas Iyer87.0Azmatullah Omarzai2--43
7374June 3,2025Narendra Modi Stadium, AhmedabadRCBPBKSFinalPBKSBowl190.09.0184.07.0completedRCB6.0000006.3243240.0Krunal PandyaShashank Singh61.0Arshdeep Singh3--40